Hydrogen Diffusion through Polymer Using Deep Reinforcement Learning

Hydrogen energy has the potential to reshape the energy landscape by replacing significant amounts of fossil fuels in many fields. Versatile storage methods have been developed to store hydrogen safely and efficiently, including physical and chemical storage. To pursue a sustainable and low-carbon future, containers with polymer liners have emerged as a low-cost hydrogen storage solution due to their chemical inertness and low permeability. Here, the understanding of long-time diffusion mechanism is critical for a rational design of next-generation hydrogen storage. In response, we have developed a computational framework with deep reinforcement learning (DRL) combined with transition state theory to investigate molecular diffusion at experimentally relevant time scale. Based on the Deep Q-network architecture and distributed training framework, DRL agent is capable of learning energy-efficient pathways in a variety of polymer morphologies. In this talk, I will discuss atomistic mechanisms of long-time molecular diffusion for polymer liner applications.